Natural language is intuitive for human communication. It relies on the spoken language, yet it is subconsciously based on body and hand gestures, where constant feedback is provided by the onlookers, for example via subtle body language, reacting to a speaker. On the other hand, human computer interfaces are not based on subtle human skills and are therefore cumbersome and unintuitive when compared to human spoken language and body language. Another example of a natural intuitive communication is music instruments such as violin or piano, where the musician uses gesture like movements to produce sound which is also used as an auditory feedback. In such a system the trained musician can play the instrument with no eye contact, with such feedback increasing the learning speed.
For many years, human-computer interactions have been mostly carried out using a standard “QWERTY” keyboard, with a screen providing a user with visual feedback of the keyboard (and mouse) input. With the constantly improving technology of computerized devices, these keyboards have now become a cumbersome means of communication. Currently, the most significant developments in the field of smart interfaces are based on computer vision using cameras and video analysis. However, this approach is limited due to the complexity of the visual data.
In recent years the touchscreen interface has become one of the most common solutions for inputting text or giving general instructions to the computer, whereby the touchscreen replaces the standard keyboard and mouse. However, using a touchscreen requires full concentration of the eyes and fingers on the screen, and an interface without the necessity of a direct view to the screen is not available today.
In search of more intuitive means for human computer interaction, other solutions such as voice recognition and gesture recognition (using a built-in microphone and/or camera) have become available in recent years; however these solutions have not been able to provide an accurate interpretation of the input. Voice recognition is based on one signal that cannot be easily deciphered (without a set of additional signals), while gesture recognition is based on computer vision and therefore highly sensitive to numerous ambient parameters.
An additional solution that has transitioned from medical applications (such as prosthesis biomechanical solutions) to generic human computer interfaces is a surface electromyography (sEMG) based device, providing recognition of coarse hand gestures for basic commands (e.g. controlling the grasp of a prosthesis) where the sEMG sensor is located near the elbow. However, such devices cannot easily detect subtle movements of the hand, for instance movement of a single finger, and therefore cannot effectively be used as an interface for a wider range of gestures. In addition, such devices require a sEMG sensor array to be located slightly below the elbow, which is an inconvenience for most users and therefore not yet widely accepted outside the medical community. Other devices are intended for the visually impaired and have a physical Braille display, but they do not provide a sEMG based interface and therefore cannot detect gestures. U.S. Pat. No. 8,447,704 describes an interface for recognition of a predefined set of general gestures based on sEMG signals.
There is therefore a need for an efficient and intuitive user interface for computerized machines that can recognize different types of subtle gestures (defined by the user) based on EMG signals. Moreover, with the development of Internet of Things (IoT) applicable devices, particularly wearable smart-watches, computer interfaces based on screens are becoming smaller and less convenient for complex interaction, due to the difficulty in closing a feedback loop between the user and the computerized device.